Feature Aggregation Networks for Image Steganalysis

Since convolutional neural networks have shown remarkable performance on various computer vision tasks, many network architectures for image steganalysis have been introduced. Many of them use fixed preprocessing filters for stable learning, which have a disadvantage of limited use of the information of the input image. The recently introduced end-to-end learning method uses a structure that limits the number of channels of feature maps close to the input and stacks residual blocks. This method has limitations in generating feature maps of various levels and resolutions that can be effective for steganalysis. We therefore propose the feature aggregation-based steganalysis networks: expand the number of channels of convolutional blocks close to the input data, aggregate feature maps of various levels and resolutions, and utilize rich information to improve steganalysis performance. In addition, the capped activation function is applied to obtain better generalization performance. The proposed method outperforms the state-of-the-art steganalysis on detection of the advanced steganography algorithms J-UNIWARD and UED, for JPEG quality factor 75 and 95.